Mobile telecommunications networks are mostly now centrally-controlled, but are evolving towards more ad-hoc and dynamic structures with cheap, low-powered nodes, such as base stations, that are auto-configurable and flexible. Controlling such networks means coping with uncertainty, not only in traffic demand but also in the structure of the network itself. Using decentralized control gives flexibility to respond locally to these uncertainties, creating self-organizing networks that rely on time-varying i.e. emergent behavior globally across the network so as to provide network-wide co-ordination. Compared with more easily monitored centrally-controlled networks, however, these decentralized networks can be difficult to predict or manipulate.
Mobile networks are expected to be used less for voice-oriented services and more for data services. As the anticipated demand for data oriented applications increases, so-called 3G systems (i.e those in accordance with Third Generation Partnership Project 3GPP standards) are expected to meet the demand as they evolve into future generation, or fourth generation (4G) networks. In order to accommodate the larger amount of traffic, 4G networks are expected to use a higher frequency band and offer channels that have bit rates that are ten times higher than that of 3G systems, as mentioned in T. Otsu, I. Okajima, N. Umeda, Y. Yamao, “Network architecture for mobile communications systems beyond IMT-2000”, IEEE Personal Communications, volume 8, number 5, pages 31-37, October 2001. The high data transmission rates that are required will necessitate the use of smaller cell sizes, and hence, potentially cause a serious increase in the cost of the network infrastructure and the cost of planning and deploying the network, as outlined in Y. Yamao, H. Suda, N. Umeda, N. Nakajima, “Radio access network design concept for the fourth generation mobile communication system”, IEEE 51st Vehicular Technology Conference Proceedings, 2000, VTC 2000-Spring Tokyo, volume 3, pages 2285-2289. This, among other reasons, has prompted suggestions to use cheap, low-powered devices that are highly flexible and are able to auto-configure to produce networks that are more ad-hoc and dynamic in structure, as described in various papers such as: A. Bira, F. Gessler, O. Queseth, R. Stridh, M. Unbehaun, J. Wu, J. Zander, “4th-Generation wireless infrastructures: scenarios and research challenges”, IEEE Personal Communications, volume 8, number 6, pages 25-31, December 2001, in J-Z. Sun, J. Sauvola, D. Howie, “Features in future: 4G visions from a technical perspective”, Global Telecommunications Conference, 2001. GLOBECOM '01, volume 6, pages 3533-3537, in B. G. Evans, K. Baughan, “Visions of 4G”, Electronics and Communications Journal, volume 12, number 6, pages 293-303, December 2000, and in Wireless World Research Forum, “Book of Visions 2001: Visions of the Wireless World”, http://www.wireless-world-research.org/BoV1.0/BoV/BoV2001v1.0.pdf
Decentralized control offers the flexibility and robustness needed to cope with the dynamic nature of such ad-hoc networks, and has many advantages over centralized control, as discussed in B. Xu, B. Walke, “Design issues of self-organizing broadband wireless networks”, Computer Networks: the International Journal of Distributed Informatique, volume 37, number 1, pages 73-81, September 2001.
However for decentralized control, wireless networks would have to evolve to structures that have many similarities with high-speed wireless LANs. These networks would be built to carry all traffic through a common packet switch transport method (such as IP). Using high frequency bands to provide the required bandwidth, the maximum size of the cells would be a lot smaller than current systems because of the higher propagation loss.
Distributed (i.e decentralized) control of wireless networks is thus becoming increasingly important in the move towards self-configuring ad-hoc type networks for 3G (UMTS) and 4G networks. This ad-hoc approach to wireless networks relies on highly distributed nodes to create flexible and highly robust network. These nodes typically handle or have the capability to handle the control of the network, which in a normal wireless network would have been done by a central controller. Functions such as routing, resource management, and auto-configuration are aspects that can be handled by ad-hoc networks. One of the challenges with an ad-hoc type network is that the amount of overhead signalling that has to be done to coordinate the whole network without a central controller can be impractical, especially if the size of the network is large. In consequence, distributed algorithms are used that make use of localized information, i.e. where the network nodes make decisions based on very limited information on the network, often only considering the information of a few neighboring nodes. These localized algorithms often rely on the global behaviors that emerge out of the simple interactions between the network's nodes to produce the self-organization that keeps the networks in check.
One of the problems that arise from the use of such localized distributed algorithms (LDA) is that the interactions and behavior of the network, although giving the desired effect, are not always straightforward. When using a centralized control approach, the behavior of the network is always known and guided through algorithms contained in the controller. Since this is not available when using localized distributed algorithms (LDA), the behavior of the network can be difficult to predict and debug if the behavior starts to go out of hand, such as the synchronization problem found in the TCP/IP protocol, and the observation of criticality in self-organized systems. LDA's can also be very limited in their functionality. Often, the evolution of the behavior of the network is one directional, where the network self-organizes towards one type of behavior, which can limit the flexibility of the network.
Various techniques are known to be used to investigate the behavior of self-organizing systems so that a better understanding on the effects of various parameters of an LDA. These methods are usually used in the physical sciences field to examine emergent phenomena. For example, Crutchfield, J. P., “The Calculi of Emergence: Computation, Dynamics and Induction,” Physica D, vol.75, no. 1-2, pp. 11-54 describes use of state reduction to extract a state transition model of the system to represent its behavior. The state reduction technique is quite complicated and requires a large sampling of the system behavior before a sufficiently accurate state transitional model can be obtained. Of course, as the sample size is not infinitely large, we can never determine for certain that the state transitional model that is produced is accurate enough.
Grasberger, P., “Toward a quantitative theory of self-generated complexity,” International Journal of Theoretical Physics, vol.25, no. 9, pp. 907-938, September 1986.], Lopez-Ruiz R., Mancini H. L., Calbet X., “A statistical measure of complexity”, Physical Letters A, pp. 321-326, 25 Dec. 1995] and Shiner J. S., Davison M., Landsberg P. T., “Simple measure for complexity”, Physical Review E, vol. 59, no. 2, pp. 1459-1464, February 1999 are papers which all describe use of entropy measurements of the system states to quantify the behavior of the system in terms of state space. Entropy-based techniques capture the behavior of the system, but also require a large sampling size of the system states to produce an accurate picture of the behavior. This is because they consider the instantaneous state of the system, and not the overall trend of the behavior, thus requiring a longer time to build up a sufficient behavior representation.
Looking at the potential problems in distributed control of a wireless network, it was realized that changes needed to be made in the way current wireless networks are designed, deployed and maintained. The base stations have to be cheap, small and unobtrusive. To reduce the increased cost of deploying a larger number of cells, the base stations also need to have the ability to self-configure to a certain extent. The process of installing a base station should be simple and straightforward, with a “plug and play” approach to installation, with the base station self-configuring different aspects of its operation such as its cell size and routing. It was realized that decentralized control offers this level of flexibility in the network and also helps with solving the scalability and robustness issues, and further decrease the cost of the network by removing the need for expensive equipment otherwise needed in centrally controlled networks. But highly decentralized networks work on self-organizing behavior, and can be unpredictable and difficult to manipulate.
For large networks composed of simple, inexpensive and highly decentralized nodes to co-ordinate themselves, they would have to be self-organizing. Self-organizing systems have the ability to evolve and adapt to retain a certain co-ordinated behavior using only localized information and relatively simple rules. It is a behavior that relies on the emergent global behavior arising from the interactions between the sub-systems. It is a behavior that can be observed in many different systems, ranging from physical, biological, sociological and mathematical systems. A main characteristic of self-organizing systems is their ability to evolve towards the same behavior pattern no matter what the initial configuration of the system is. Self-organizing behavior would be useful applied to co-ordination of a network. One can imagine a network where the base stations, using basic rules, would be able to settle down to the same behavior regardless of what the initial conditions are during initial deployment, or what changes are made to the network.
While self-organization has a very promising application in wireless networks, there are several problems and challenges that can arise from the use of self-organizing networks. Even though self-organizing networks are able to maintain a co-ordinating behavior, under certain conditions, very sudden changes can occur. These sudden changes in the system behavior, sometimes called self-organized criticality, occurs when certain parameters of the system are changed and changes the principle behavior of the system, causing a phase transition, flipping it from, say, a static behavior to a chaotic one as described in P. Bak, C. Tang, K. Wiesenfeld, “Self-organized criticality”, Physical Review a (General Physics), volume 38, number 1, pp. 364-74, 1 Jul. 1988. Examples of these critical behaviors have been observed in many different systems, including telecommunications networks as described in R. V. Sole, S. Valverde, “Information transfer and phase transitions in a model of Internet traffic” Physica A, volume 289, number 3-4, pages 595-605, 15 Jan. 2001. One of the features of criticality is that only a slight incremental change of a system parameter or structure can cause this change of behavior. This characteristic is obviously not desirable in a wireless network where such a catastrophic failure could bring down the whole network very suddenly and without warning.
Another challenge that is posed by using self-organizing networks is the difficulty of designing the algorithms themselves. Controlling the behavior of centralized networks is more straightforward as the behavior of the whole network is always known and can be changed directly. In self-organizing networks, producing the algorithm that would give rise to the desired emergent behavior for self-organization requires more careful consideration, since producing the desired emergent behavior cannot be done by simply telling the network nodes what to do directly.
The unpredictability of self-organizing systems thus poses difficulties when applied to wireless networks. It would be advantageous to be able to determine how the network would behave under different scenarios during the design of the network, for example to make sure that during the operation of the network there is some sufficient warning of unpredicted network behavior. Also if something goes wrong, it would be useful to be able to readily analyze the network behavior to find out the cause of the network failure.
To review, the use of self-organization in for example 4G networks is important. Mobile networks are evolving from using a centrally controlled architecture towards one with a more ad-hoc and dynamic character. In such networks, the coordination and management system has to cope with a network hierarchy, connectivity and node availability that is not clear and changes continuously. Distributed, decentralized controlled networks are ideal for use under these circumstances. Self-organizing behavior of these networks is capable of providing a very flexible and robust structure without using very complicated and expensive hardware and software, giving network providers the ability of providing high bandwidth services whilst keeping the cost of network deployment and management low. The difficulty of predicting the behavior of self-organizing networks, however, is an issue that can cause problems in decentralized networks. Self-organizing systems are sometimes known to exhibit critical behavior where, under certain circumstances, the behavior of the network changes very drastically. The difficulty of predicting the behavior also makes the design and optimization of these networks awkward.